Skin color modeling for face detection and segmentation: a review and a new approach

Chen, Wei; Wang, Ke; Jiang, Haifeng; Li, Ming

doi:10.1007/s11042-014-2328-0

Cited by 30 publications

(11 citation statements)

References 56 publications

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“…However, the viola-jones face detection is not robust to pose, illumination changes, and especially occlusion. Color features have also been implemented to detect the face in the images [ 4 – 6 ].…”

Section: Related Workmentioning

confidence: 99%

Masked face recognition with convolutional neural networks and local binary patterns

Vu¹,

Nguyen²,

Pham³

2021

Appl Intell

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Face recognition is one of the most common biometric authentication methods as its feasibility while convenient use. Recently, the COVID-19 pandemic is dramatically spreading throughout the world, which seriously leads to negative impacts on people’s health and economy. Wearing masks in public settings is an effective way to prevent viruses from spreading. However, masked face recognition is a highly challenging task due to the lack of facial feature information. In this paper, we propose a method that takes advantage of the combination of deep learning and Local Binary Pattern (LBP) features to recognize the masked face by utilizing RetinaFace, a joint extra-supervised and self-supervised multi-task learning face detector that can deal with various scales of faces, as a fast yet effective encoder. In addition, we extract local binary pattern features from masked face’s eye, forehead and eyebow areas and combine them with features learnt from RetinaFace into a unified framework for recognizing masked faces. In addition, we collected a dataset named COMASK20 from 300 subjects at our institution. In the experiment, we compared our proposed system with several state of the art face recognition methods on the published Essex dataset and our self-collected dataset COMASK20. With the recognition results of 87% f1-score on the COMASK20 dataset and 98% f1-score on the Essex dataset, these demonstrated that our proposed system outperforms Dlib and InsightFace, which has shown the effectiveness and suitability of the proposed method. The COMASK20 dataset is available on https://github.com/tuminguyen/COMASK20 for research purposes.

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Section: Related Workmentioning

confidence: 99%

Masked face recognition with convolutional neural networks and local binary patterns

Vu¹,

Nguyen²,

Pham³

2021

Appl Intell

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“…We already showed that q = 0 in Eq. (12). That means the distance between p j M and all p j in R (ALGORITHM 2) = 0.…”

Section: Prediction By Interpolation Using Idwmentioning

confidence: 99%

PKT: fast color-based spatial model for human skin detection

Samson

2021

Multimed Tools Appl

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We present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.

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“…Skin detection is the process of finding skin-colored pixels and regions from images and videos. It is a very interesting problem and typically serves as a pre-processing step for further applications like face detection, gestures detection, semantic filtering of web contents and so on [5,26,2,15,10].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Skin Detection by Network With Mutual Guidance

Shi²,

Wang³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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In this paper we present a new data-driven method for robust skin detection from a single human portrait image. Unlike previous methods, we incorporate human body as a weak semantic guidance into this task, considering acquiring large-scale of human labeled skin data is commonly expensive and time-consuming. To be specific, we propose a dual-task neural network for joint detection of skin and body via a semi-supervised learning strategy. The dualtask network contains a shared encoder but two decoders for skin and body separately. For each decoder, its output also serves as a guidance for its counterpart, making both decoders mutually guided. Extensive experiments were conducted to demonstrate the effectiveness of our network with mutual guidance, and experimental results show our network outperforms the state-of-the-art in skin detection.

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Skin color modeling for face detection and segmentation: a review and a new approach

Cited by 30 publications

References 56 publications

Masked face recognition with convolutional neural networks and local binary patterns

Masked face recognition with convolutional neural networks and local binary patterns

PKT: fast color-based spatial model for human skin detection

Semi-Supervised Skin Detection by Network With Mutual Guidance

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